OpenRaiser/Intern-Atlas huggingface dataset:
Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing
Venue: arXiv cs.IR 2018
Year: 2018

Knowledge tracing is a sequence prediction problem where the goal is to predict the outcomes of students over questions as they are interacting with a learning platform. By tracking the evolution of the knowledge of some student, one can optimize instruction. Existing methods are either based on temporal latent variable models, or factor analysis with temporal features. We here show that factorization machines (FMs), a model for regression or classification, encompasses several existing models in the educational literature as special cases, notably additive factor model, performance factor model, and multidimensional item response theory. We show, using several real datasets of tens of thousands of users and items, that FMs can estimate student knowledge accurately and fast even when student data is sparsely observed, and handle side information such as multiple knowledge components and number of attempts at item or skill level. Our approach allows to fit student models of higher dimension than existing models, and provides a testbed to try new combinations of features in order to improve existing models.
Quantifying Link Stability in Ad Hoc Wireless Networks Subject to Ornstein-Uhlenbeck Mobility
Venue: arXiv cs.NI 2018
Year: 2018

The performance of mobile ad hoc networks in general and that of the routing algorithm, in particular, can be heavily affected by the intrinsic dynamic nature of the underlying topology. In this paper, we build a new analytical/numerical framework that characterizes nodes' mobility and the evolution of links between them. This formulation is based on a stationary Markov chain representation of link connectivity. The existence of a link between two nodes depends on their distance, which is governed by the mobility model. In our analysis, nodes move randomly according to an Ornstein-Uhlenbeck process using one tuning parameter to obtain different levels of randomness in the mobility pattern. Finally, we propose an entropy-rate-based metric that quantifies link uncertainty and evaluates its stability. Numerical results show that the proposed approach can accurately reflect the random mobility in the network and fully captures the link dynamics. It may thus be considered a valuable performance metric for the evaluation of the link stability and connectivity in these networks.
Automated Remote Patient Monitoring: Data Sharing and Privacy Using Blockchain
Venue: arXiv cs.CY 2018
Year: 2018

The revolution of Internet of Things (IoT) devices and wearable technology has opened up great possibilities in remote patient monitoring. To streamline the diagnosis and treatment process, healthcare professionals are now adopting the wearable technology. However, these technologies also pose grave privacy risks and security concerns about the transfer and the logging of data transactions. One solution to protect privacy in healthcare is the use of blockchain technology. However, one of the primary problems with blockchain is its highly limited scalability. In this work here, we propose the utilization of a blockchain based protocol to provide secure management and analysis of data. In this paper we use recently introduced PoW based protocol GHOSTDAG, that generalizes Satoshi's blockchain to a direct acyclic graph of blocks (blockDAG) and provides high throughput while also avoiding the security-scalability problem. We use two blockchains based on the original GHOSTDAG protocol, one that is private and one that is public. Using a private blockchain, we create a system where we use smart contracts to analyze patient health data. If the smart contract for any reason issues an alert for an abnormal reading then the system makes the record of that event to the public blockchain. This would resolve the privacy and security vulnerabilities associated with remote patient monitoring and also the limited scalability problem of Satoshi's original blockchain.
A holistic look at requirements engineering practices in the gaming industry
Venue: arXiv cs.SE 2018
Year: 2018

In this work we present an account of the status of requirements engineering in the gaming industry. Recent papers in the area were surveyed. Characterizations of the gaming industry were deliberated upon by portraying its relations with the market industry. Some research directions in the area of requirements engineering in the gaming industry were also mentioned.
Triple consistency loss for pairing distributions in GAN-based face synthesis
Venue: arXiv cs.CV 2018
Year: 2018

Generative Adversarial Networks have shown impressive results for the task of object translation, including face-to-face translation. A key component behind the success of recent approaches is the self-consistency loss, which encourages a network to recover the original input image when the output generated for a desired attribute is itself passed through the same network, but with the target attribute inverted. While the self-consistency loss yields photo-realistic results, it can be shown that the input and target domains, supposed to be close, differ substantially. This is empirically found by observing that a network recovers the input image even if attributes other than the inversion of the original goal are set as target. This stops one combining networks for different tasks, or using a network to do progressive forward passes. In this paper, we show empirical evidence of this effect, and propose a new loss to bridge the gap between the distributions of the input and target domains. This "triple consistency loss", aims to minimise the distance between the outputs generated by the network for different routes to the target, independent of any intermediate steps. To show this is effective, we incorporate the triple consistency loss into the training of a new landmark-guided face to face synthesis, where, contrary to previous works, the generated images can simultaneously undergo a large transformation in both expression and pose. To the best of our knowledge, we are the first to tackle the problem of mismatching distributions in self-domain synthesis, and to propose "in-the-wild" landmark-guided synthesis. Code will be available at https://github.com/ESanchezLozano/GANnotation
Towards Connecting Experiences during Collocated Events through Data Mining and Visualization
Venue: arXiv cs.HC 2018
Year: 2018

Themed collocated events, such as conferences, workshops, and seminars, invite people with related life experiences to connect with each other. In this era when people record lives through the Internet, individual experiences exist in different forms of digital contents. People share digital life records during collocated events, such as sharing blogs they wrote, Twitter posts they forwarded, and books they have read. However, connecting experiences during collocated events are challenging. Not only one is blind to the large contents of others, identifying related experiential items depends on how well experiences are retrieved. The collection of personal contents from all participants forms a valuable group repository, from which connections between experiences can be mined. Visualizing same or related experiences inspire conversations and support social exchange. Common topics in group content also help participants generate new perspectives about the collocated group. Advances in machine learning and data visualization provide automated approaches to process large data and enable interactions with data repositories. This position paper promotes the idea of event mining: how to utilize state-of-the-art data processing and visualization techniques to design event mining systems for connecting experiences during collocated activities. We discuss empirical and constructive problems in this design space, and our preliminary study of deploying a tabletop-based system, BlogCloud, which supports experience re-visitation and exchange with machine-learning and data visualization.
A Natural Language Interface with Relayed Acoustic Communications for Improved Command and Control of AUVs
Venue: arXiv cs.HC 2018
Year: 2018

Autonomous underwater vehicles (AUVs) are being tasked with increasingly complex missions. The acoustic communications required for AUVs are, by the nature of the medium, low bandwidth while adverse environmental conditions underwater often mean they are also intermittent. This has motivated development of highly autonomous systems, which can operate independently of their operators for considerable periods of time. These missions often involve multiple vehicles leading not only to challenges in communications but also in command and control (C2). Specifically operators face complexity in controlling multi-objective, multi-vehicle missions, whilst simultaneously facing uncertainty over the current status and safety of several remote high value assets. Additionally, it may be required to perform command and control of these complex missions in a remote control room. In this paper, we propose a combination of an intuitive, natural language operator interface combined with communications that use platforms from multiple domains to relay data over different mediums and transmission modes, improving command and control of collaborative and fully autonomous missions. In trials, we have demonstrated an integrated system combining working prototypes with established commercial C2 software that enables the use of a natural language interface to monitor an AUV survey mission in an on-shore command and control centre.
A Primal Decomposition Method with Suboptimality Bounds for Distributed Mixed-Integer Linear Programming
Venue: arXiv cs.SY 2018
Year: 2018

In this paper we deal with a network of agents seeking to solve in a distributed way Mixed-Integer Linear Programs (MILPs) with a coupling constraint (modeling a limited shared resource) and local constraints. MILPs are NP-hard problems and several challenges arise in a distributed framework, so that looking for suboptimal solutions is of interest. To achieve this goal, the presence of a linear coupling calls for tailored decomposition approaches. We propose a fully distributed algorithm based on a primal decomposition approach and a suitable tightening of the coupling constraints. Agents repeatedly update local allocation vectors, which converge to an optimal resource allocation of an approximate version of the original problem. Based on such allocation vectors, agents are able to (locally) compute a mixed-integer solution, which is guaranteed to be feasible after a sufficiently large time. Asymptotic and finite-time suboptimality bounds are established for the computed solution. Numerical simulations highlight the efficacy of the proposed methodology.
Validating Hyperspectral Image Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Hyperspectral satellite imaging attracts enormous research attention in the remote sensing community, hence automated approaches for precise segmentation of such imagery are being rapidly developed. In this letter, we share our observations on the strategy for validating hyperspectral image segmentation algorithms currently followed in the literature, and show that it can lead to over-optimistic experimental insights. We introduce a new routine for generating segmentation benchmarks, and use it to elaborate ready-to-use hyperspectral training-test data partitions. They can be utilized for fair validation of new and existing algorithms without any training-test data leakage.
EPDA: Enhancing Privacy-Preserving Data Authentication for Mobile Crowd Sensing
Venue: arXiv cs.CR 2018
Year: 2018

As a popular application, mobile crowd sensing systems aim at providing more convenient service via the swarm intelligence. With the popularity of sensor-embedded smart phones and intelligent wearable devices, mobile crowd sensing is becoming an efficient way to obtain various types of sensing data from individuals, which will make people's life more convenient. However, mobile crowd sensing systems today are facing a critical challenge, namely the privacy leakage of the sensitive information and valuable data, which can raise grave concerns among the participants. To address this issue, we propose an enhanced secure certificateless privacy-preserving verifiable data authentication scheme for mobile crowd sensing, named EPDA. The proposed scheme provides unconditional anonymous data authentication service for mobile crowd sensing, by deploying an improved certificateless ring signature as the cryptogram essential, in which the big sensing data should be signed by one of legitimate members in a specific group and could be verified without exposing the actual identity of the participant. The formal security proof demonstrates that EPDA is secure against existential forgery under adaptive chosen message and identity attacks in random oracle model. Finally, extensive simulations are conducted. The results show that the proposed EPDA efficiently decreases computational cost and time consumption in the sensing data authentication process.
MDBV: Monitoring Data Batch Verification for Survivability of Internet of Vehicles
Venue: arXiv cs.CR 2018
Year: 2018

Along with the development of vehicular sensors and wireless communication technology, Internet of Vehicles (IoV) is emerging that can improve traffic efficiency and provide a comfortable driving environment. However, there is still a challenge how to ensure the survivability of IoV. Fortunately, this goal can be achieved by quickly verifying real-time monitoring data to avoid network failure. Aggregate signature is an efficient approach to realize quick data verification quickly. In this paper, we propose a monitoring data batch verification scheme based on an improved certificateless aggregate signature for IoV, named MDBV. The size of aggregated verification message is remain roughly constant even as the increasing number of vehicles in MDBV. Additionally, MDBV is proved to be secure in the random oracle model assuming the intractability of the computational Diffie-Hellman problem. In consideration of the network survivability and performance, the proposed MDBV can decrease the computation overhead and is more suitable for IoV.
Semantic and Contrast-Aware Saliency
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we proposed an integrated model of semantic-aware and contrast-aware saliency combining both bottom-up and top-down cues for effective saliency estimation and eye fixation prediction. The proposed model processes visual information using two pathways. The first pathway aims to capture the attractive semantic information in images, especially for the presence of meaningful objects and object parts such as human faces. The second pathway is based on multi-scale on-line feature learning and information maximization, which learns an adaptive sparse representation for the input and discovers the high contrast salient patterns within the image context. The two pathways characterize both long-term and short-term attention cues and are integrated dynamically using maxima normalization. We investigate two different implementations of the semantic pathway including an End-to-End deep neural network solution and a dynamic feature integration solution, resulting in the SCA and SCAFI model respectively. Experimental results on artificial images and 5 popular benchmark datasets demonstrate the superior performance and better plausibility of the proposed model over both classic approaches and recent deep models.
Energy-Efficient Offloading in Mobile Edge Computing with Edge-Cloud Collaboration
Venue: arXiv cs.DC 2018
Year: 2018

Multiple access mobile edge computing is an emerging technique to bring computation resources close to end mobile users. By deploying edge servers at WiFi access points or cellular base stations, the computation capabilities of mobile users can be extended. Existing works mostly assume the remote cloud server can be viewed as a special edge server or the edge servers are willing to cooperate, which is not practical. In this work, we propose an edge-cloud cooperative architecture where edge servers can rent for the remote cloud servers to expedite the computation of tasks from mobile users. With this architecture, the computation offloading problem is modeled as a mixed integer programming with delay constraints, which is NP-hard. The objective is to minimize the total energy consumption of mobile devices. We propose a greedy algorithm as well as a simulated annealing algorithm to effectively solve the problem. Extensive simulation results demonstrate that, the proposed greedy algorithm and simulated annealing algorithm can achieve the near optimal performance. On average, the proposed greedy algorithm can achieve the same application completing time budget performance of the Brute Force optional algorithm with only 31\% extra energy cost. The simulated annealing algorithm can achieve similar performance with the greedy algorithm.
A Fully Automated System for Sizing Nasal PAP Masks Using Facial Photographs
Venue: arXiv cs.CV 2018
Year: 2018

We present a fully automated system for sizing nasal Positive Airway Pressure (PAP) masks. The system is comprised of a mix of HOG object detectors as well as multiple convolutional neural network stages for facial landmark detection. The models were trained using samples from the publicly available PUT and MUCT datasets while transfer learning was also employed to improve the performance of the models on facial photographs of actual PAP mask users. The fully automated system demonstrated an overall accuracy of 64.71% in correctly selecting the appropriate mask size and 86.1% accuracy sizing within 1 mask size.
A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI
Venue: arXiv cs.CV 2018
Year: 2018

Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging techniques available for clinical applications. However, the rather slow speed of MRI acquisitions limits the patient throughput and potential indi cations. Compressive Sensing (CS) has proven to be an efficient technique for accelerating MRI acquisition. The most widely used CS-MRI model, founded on the premise of reconstructing an image from an incompletely filled k-space, leads to an ill-posed inverse problem. In the past years, lots of efforts have been made to efficiently optimize the CS-MRI model. Inspired by deep learning techniques, some preliminary works have tried to incorporate deep architectures into CS-MRI process. Unfortunately, the convergence issues (due to the experience-based networks) and the robustness (i.e., lack real-world noise modeling) of these deeply trained optimization methods are still missing. In this work, we develop a new paradigm to integrate designed numerical solvers and the data-driven architectures for CS-MRI. By introducing an optimal condition checking mechanism, we can successfully prove the convergence of our established deep CS-MRI optimization scheme. Furthermore, we explicitly formulate the Rician noise distributions within our framework and obtain an extended CS-MRI network to handle the real-world nosies in the MRI process. Extensive experimental results verify that the proposed paradigm outperforms the existing state-of-the-art techniques both in reconstruction accuracy and efficiency as well as robustness to noises in real scene.
Encoding Implicit Relation Requirements for Relation Extraction: A Joint Inference Approach
Venue: arXiv cs.CL 2018
Year: 2018

Relation extraction is the task of identifying predefined relationship between entities, and plays an essential role in information extraction, knowledge base construction, question answering and so on. Most existing relation extractors make predictions for each entity pair locally and individually, while ignoring implicit global clues available across different entity pairs and in the knowledge base, which often leads to conflicts among local predictions from different entity pairs. This paper proposes a joint inference framework that employs such global clues to resolve disagreements among local predictions. We exploit two kinds of clues to generate constraints which can capture the implicit type and cardinality requirements of a relation. Those constraints can be examined in either hard style or soft style, both of which can be effectively explored in an integer linear program formulation. Experimental results on both English and Chinese datasets show that our proposed framework can effectively utilize those two categories of global clues and resolve the disagreements among local predictions, thus improve various relation extractors when such clues are applicable to the datasets. Our experiments also indicate that the clues learnt automatically from existing knowledge bases perform comparably to or better than those refined by human.
The trouble with tensor ring decompositions
Venue: arXiv cs.NA 2018
Year: 2018

The tensor train decomposition decomposes a tensor into a "train" of 3-way tensors that are interconnected through the summation of auxiliary indices. The decomposition is stable, has a well-defined notion of rank and enables the user to perform various linear algebra operations on vectors and matrices of exponential size in a computationally efficient manner. The tensor ring decomposition replaces the train by a ring through the introduction of one additional auxiliary variable. This article discusses a major issue with the tensor ring decomposition: its inability to compute an exact minimal-rank decomposition from a decomposition with sub-optimal ranks. Both the contraction operation and Hadamard product are motivated from applications and it is shown through simple examples how the tensor ring-rounding procedure fails to retrieve minimal-rank decompositions with these operations. These observations, together with the already known issue of not being able to find a best low-rank tensor ring approximation to a given tensor indicate that the applicability of tensor rings is severely limited.
The Price of Governance: A Middle Ground Solution to Coordination in Organizational Control
Venue: arXiv cs.MA 2018
Year: 2018

Achieving coordination is crucial in organizational control. This paper investigates a middle ground solution between decentralized interactions and centralized administrations for coordinating agents beyond inefficient behavior. We first propose the price of governance (PoG) to evaluate how such a middle ground solution performs in terms of effectiveness and cost. We then propose a hierarchical supervision framework to explicitly model the PoG, and define step by step how to realize the core principle of the framework and compute the optimal PoG for a control problem. Two illustrative case studies are carried out to exemplify the applications of the proposed framework and its methodology. Results show that by properly formulating and implementing each step, the hierarchical supervision framework is capable of promoting coordination among agents while bounding administrative cost to a minimum in different kinds of organizational control problems.
A Very Brief and Critical Discussion on AutoML
Venue: arXiv cs.AI 2018
Year: 2018

This contribution presents a very brief and critical discussion on automated machine learning (AutoML), which is categorized here into two classes, referred to as narrow AutoML and generalized AutoML, respectively. The conclusions yielded from this discussion can be summarized as follows: (1) most existent research on AutoML belongs to the class of narrow AutoML; (2) advances in narrow AutoML are mainly motivated by commercial needs, while any possible benefit obtained is definitely at a cost of increase in computing burdens; (3)the concept of generalized AutoML has a strong tie in spirit with artificial general intelligence (AGI), also called "strong AI", for which obstacles abound for obtaining pivotal progresses.